1. Field of the Invention
The present invention relates to an information recognition system and control systems using the same recognition system, and more specifically to an information recognition system for processing large scale information such as image data or speech data that are huge in the information quantity and redundant in the representation format.
2. Description of the Prior Art
The conventional information processing system has been so far constructed by a CPU based upon a Neumann-type computing system usually. Further, the processing speed of the information has been improved by increasing the processing speed of the CPU itself or by connecting a plurality of CPUs in parallel to each other for simultaneous processing.
In the conventional processing system, however, since the systems is not provided with means for learning the information recognition criteria for itself, it has been so far necessary to give the information recognition criteria to the processing system previously in the form of programs manually. However, in order to recognize large scale data, a great number of recognition rules must be given to the processing system without inconsistency, with the result that it has been practically impossible to realize the large scale information recognition processing.
On the other hand, it has been well known that there exists a neural network as an apparatus for learning the recognition criterion automatically. The neural network is composed of a great number of neurons. The input and output relationship of each of the neurons is given in the form of Zigmond function, for instance. Further, these neurons are coupled to each other hierarchically via appropriate weight coefficients adjusted in such a way that the input and output signal relationship of the whole circuit can be approximated to the input-teaching data relationship. As the adjusting method, a back propagation method is well known such that the input data and the teaching data are given repeatedly in order to acquire the recognition criteria automatically.
In the conventional neural network, however, the input and output signal relationship of the whole circuit is learned integrally, in both the cases where the network is realized as software by the computer and where the network is realized as hardware by analog or digital circuits. Accordingly, when the large scale data are learned to acquire a great number of recognition rules, since the number of learning repetitions increases greatly, it has been also impossible to realize the large scale information recognition processing even with the use of the neural network.
As described above, the conventional information recognition system is the Neumann-type recognition system in which it is difficult to determine the recognition rules, or the integral-type neural network which can learn the recognition rules automatically but is difficult for processing the large scale data.
In the case where large scale data are processed by the conventional information recognition system, in order to set the information processing load as low as possible, the basic steps are to select the feature variables along the object of the final classification recognition and to compress the information in accordance with the information compression processing represented by the feature extraction. In practice, when appropriate feature variables (some scaler quantities, in many cases) can be selected, it is possible to fairly reduce the overall processing load thereof.
In the recognition systems actually required in the process control, however, it is not only difficult to always select the feature variables having sufficient information required for the final object, but also there exists the case where the effective feature variables cannot be obtained under the actual environment of the recognition processing execution.
To overcome the above-mentioned problem, therefore, various parallel processing techniques have been recently adopted in practice with the advance of the improvement in the computation capability. Among these, in particular, the neural network is expected as an effective method of classifying and recognizing large scale data in both the learning with teaching-data type network and the learning without teaching-data type neural network. The neural network technique has been so far widely applied in the fields of character and speech recognition. However, it is reported that the overall recognition rate can be improved by combination of the conventional recognition processing method with the neural network technique. For instance, there exists a system in which a character recognizing apparatus constructed by a conventional Neumann type computer is combined with a neural network recognizing apparatus. In this system, although the recognition rate of 95% or more is obtained, the neural network is used only to improve the recognition rate by 2 to 3%.
In the general image recognition, however, it is rather difficult to utilize the conventional recognition processing technique effectively as described above, so that the recognition processing load is often increased markedly on the neural network side. In addition, in the case of the learning with teaching-data network widely used in the image recognition processing, it is very difficult to decide an effective network architecture in the learning processing (which is important process for realizing the detailed recognition and classification system). That is, there exists such a decisive drawback that the learning itself cannot be converged abruptly with increasing number of data to be classified and/or recognized.
To overcome the above-mentioned problem, it may be possible to adopt the learning without teaching-data type network (which is less in stagnation of the overall learning processing) as the central network architecture. In the case of the learning without teaching-data type network, although being effective to the rough category classification, it is often difficult to classify or recognize the detailed categories.
On the background as described above, recently, a combination of the network without teaching data and the network with teaching-data has been proposed such that feature extraction processing is executed previously to some extent by the network without teaching-data and then the output is given to the network with teaching-data for learning of the data classification capability.
In the above-mentioned combined system as described above, however, when the amount of data to be processed increases hugely, there still exists a problem in that it is difficult to construct a system which can sufficiently recognize and classify the huge data under a practical load (e.g., the number of design items) considered by the current computer technology.
There has been so far known such a system which is the combination of the above two types of networks. This system includes a learning without teaching-data type neural network and a plurality of learning with teaching-data type neural networks. In the case of the learning without teaching-data type neural network, since the processing required for the architecture is only one, no wasteful processing exists. In the case of the learning with teaching-data type neural networks, however, since the network processing apparatuses corresponding to the number of the processing steps are required (in spite of the network processing in the same architecture), there exists inevitably a wasteful processing.
In particular, when the neural network processing apparatus is an individual processing board, the hardware resources required for the system construction excessively increase, so that it becomes impossible to coexist with other systems without any practicability.
Here, in the case of the large scale neural networks having great numbers of the inputs and outputs and the intermediate layers are constructed, if each neural network element is constructed by a single hardware element one by one, the number of the hardware elements becomes huge and thereby the number of connections also increases extraordinarily. As a result, the reliability of the neural network system is deteriorated and in addition a very wide space is required for the hardware.
To overcome the above-mentioned problem, it is possible to construct an apparent neural network composed of a plurality of neural network elements in the form of software, by controlling the input/output signals of the single neural network hardware element in accordance with computer software. In more detail, the weight coefficient data between the neural network elements and the input/output signal data of the respective neural network elements are read into a computer through an external bus, calculated by a CPU of the computer, and then transferred to the neural network elements through the external bus as the connection weight data between the neural network elements and the input/output signal data.
In this method, however, since it takes much time to transfer data between the neural network and the computer through an external bus, and further since the general purpose CPU of the computer does not necessarily function as an optimum neural network element, a huge time is required for the large scale neural network learning and the data propagation in the forward propagation direction. That is the first point of the problem related to the invention described here.
Next, in order to describe an operation of a new data recognition system to a man-machine interface, some problems of a graphical man-machine interface apparatus will be described hereinbelow.
For instance, a human face changes in various ways according to his feeling or mind. Therefore, it is possible to consider that the human face includes a lot of useful information. In the conventional graphical man-machine interface apparatus, however, the operation thereof has been executed irrespective of the expression of the operator's face, that is, the various operator's information. As a result, some problems have been so far proposed from the user's or operator's standpoint.
One of the above-mentioned problems relates to a cash dispenser installed in various banking organizations such as banks or post offices. In the cash dispenser, the operation required for transferring money to another bank is complicated in particular. Although the guidance of operation procedure and inputted contents are displayed on a display picture, the displayed instruction is often difficult for some operators to understand. That is, the operation may be simple and easy for the persons accustomed thereto. However, this man-machine interface apparatus is very troublesome to the person who operates the apparatus on rare occasions or who is poor in handling machines.
In other words, in the conventional man-machine interface apparatus, a predetermined operation sequence is required for the operator irrespective of the operator's skill. To facilitate the operation, the graphical user interfaces have been so far widely used. In this case, however, some knowledge over a predetermined level is required for the operator in advance. Further, there exists an interface apparatus such that two different operation sequences are prepared in advance according to the operators different in skill so that the operator can select any one of them. However, this interface apparatus is still not satisfactory. The reason is as follows: Since the operation is explained in accordance with a predetermined sequence irrespective of the feeling of the operator now operating the system, the operation sequence is not well understandable for the non-skilled person so that stress may be caused, or in contrast with this, the skilled person may be irritated. Further, when the apparatus is operated by the non-skilled person, since the apparatus workability is lowered, another skilled advisor is necessary. As described above, when the man-machine interface apparatus is used as machines for selling products, since the machine makes a cool impression on the user's mind, the man-machine interface apparatus has been so far used for only automatic vending machines.
Next, another problem concerned with man-machine interface apparatus will be described hereinbelow.
Although the above-mentioned problems are widely noticed in the field of the man-machine interface apparatus, there exists another problem with respect to the person skilled in computer operation to some extent. The problem is related to the operation of entering data to the machine through a display picture, in particular with respect to use of a pointing device. As the pointing devices, a touch pen, mouse, etc. are so far known. In these pointing devices, a pointer must be shifted by the operator's hand, so that a relatively large motion is required for the operator whenever the pointer is required to be shifted. For instance, when a cursor on a display picture is moved with the use of a mouse, the operator must first take a mouse with his hand, move the mouse on a predetermined place (a mouse pad, a desk, etc.), and then click the button on when the cursor is located. In these operations, since some motional actions are required for the operator, there exists the case where the cursor cannot be shifted to a desired position along a considered locus, thus causing a vicious cycle of irritation and erroneous operation, in spite of the fact that a quick operation is required for the operator.
In summary, in the conventional man-machine interface apparatus, since the manipulation is not related to the feeling of the operator's face, there exists a problem in that the manipulatability of the interface apparatus is not satisfactory.
Next, in order to describe another application of the new data recognition system to a control system used in some industrial plants, some problems of general control system or controller will be described hereinbelow.
A one-loop controller has been so far known, by which various operation parameters of a plant can be controlled using image data of an object to be controlled.
In the conventional one-loop controller, the operation of a plant has been automatized by inputting various measured values such as pressure, flow rates, temperatures, etc. and further controlling the process variables by operating various actuators with proportional, integral and differential calculations as feed-back control action.
In the conventional one-loop controller, however, since only one-dimensional information is processed, it has been impossible to adjust the operation parameters of a plant using the operating conditions observed by the operator; that is, image (two-dimensional) information.
Further, when fuzzy inference is adopted as the control algorithm for the one-loop controller, the rules and the membership functions used for the fuzzy inference must be adjusted at the initial setting stage in many cases. However, since the above-mentioned one-loop controller is not provided with on-line adjustment functions, great labor is required to adjust the rules and the membership functions.
Further, in the conventional one-loop controller, since several-hundred control loops are controlled simultaneously to realize a simple maintenance, an independent controller is allocated to each control loop, and these controllers are used as one-loop controller in combination. That is, a plurality of one-loop controllers are mounted on a rack so that a great number of loops can be monitored by a single panel. Therefore, the shape of the controller panel is usually narrow in the width direction and long in the depth direction. Since the front surface of the controller panel is narrow, the plant operation parameters (controlled variables, set point values, manipulated variables of plants, etc.) are displayed on simple meters arranged on the control panel, so that a plotter, for instance, is additionally necessary to check the time trends. Further, even if monitor screens are provided in the front surface of the controller panel, the displayed picture surfaces are narrow, so that the manipulatability is low.
Further, in the case of the visual feedback control based upon image recognition and image information with the use of the neural network, it is necessary to first execute the learning operation of the neural network by use of a great number of teaching data. A problem which has arisen in the conventional feedback control will be explained. The image recognition by use of the neural network can be executed in accordance with the following procedure: First, features are extracted from a great number of learning image data, and the image data are classified according to several categories on the basis of the extracted features (in the case of image data having the same features with respect to several evaluation criteria). Thereafter, a great number of learning image data are inputted to the neural network, and the neural network learning is executed until the outputs of the neural network can be roughly equalized to each other for the inputted learning image data belonging to the same category. In other words, since the image recognition precision and the convergent speed of the neural network are dependent upon the classification precision of the learning image data, when the feature extraction and the classification method of the learning data are not appropriate, it is impossible to allow the neural network to learn image data, so that the image recognition itself of the neural network is deteriorated.
Conventionally, when the image data are recognized by the neural network, a skilled operator observes image data one by one independently and compares the observed image data with appropriate data at need in order to classify the image data into several categories. In this case, however, since the number of image data required for the neural network is huge, so that it is extremely difficult for the skilled operator to classify the image data on the basis of universal criteria. As a result, there exists many cases where the image data having the similar features are classified into other categories, so that it has been difficult to improve the image data learning efficiency and the image recognition precision.
There has been another problem arisen in the conventional feedback control. When the shape recognition is executed using image information and the neural network learning, since there exist no indices for setting appropriate initial values of the coupling weights between the mutual nodes of the neural network, the initial values have been so far determined on the basis of random numbers. However, a long time is required for such an initial learning that the coupling weights must be tuned until a constant effect can be obtained after the random numbers have been set, so that the efficiency is low. In addition, when the shape recognition is executed on the basis of image information of low S/N ratio, the outputs of the neural network are unstable, so that the system reliability is low.